2017/04/26 by Daniele De Martino, De Martino, Daniele, Andrea De Martino +1
Biochemistry, Genetics and Molecular Biology · #Biological Physics (physics.bio-ph) #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Physical sciences #Gene Regulatory Network Analysis #Metabolomics and Mass Spectrometry Studies #Microbial Metabolic Engineering and Bioproduction #Molecular Networks (q-bio.MN) #Statistical Mechanics (cond-mat.stat-mech)
paper · pdf · doi:10.48550/arxiv.1704.08087
openalex publication_date 2017/04/26 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
We consider the problem of inferring the probability distribution of flux\nconfigurations in metabolic network models from empirical flux data. For the\nsimple case in which experimental averages are to be retrieved, data are\ndescribed by a Boltzmann-like distribution (\∝ eF/T) where F is a\nlinear combination of fluxes and the `temperature' parameter T\≥ 0 allows\nfor fluctuations. The zero-temperature limit corresponds to a Flux Balance\nAnalysis scenario, where an objective function (F) is maximized. As a test,\nwe have inverse modeled, by means of Boltzmann learning, the catabolic core of\nEscherichia coli in glucose-limited aerobic stationary growth conditions.\nEmpirical means are best reproduced when F is a simple combination of biomass\nproduction and glucose uptake and the temperature is finite, implying the\npresence of fluctuations. The scheme presented here has the potential to\ndeliver new quantitative insight on cellular metabolism. Our implementation is\nhowever computationally intensive, and highlights the major role that effective\nalgorithms to sample the high-dimensional solution space of metabolic networks\ncan play in this field.\n